Internal Seminar at COSBI
This week, COSBI hosted an internal research seminar by Roberto Visintainer, focused on the prediction of anti-tuberculosis treatment regimen efficacy using relapse mouse model data.
During the seminar, Roberto presented how machine learning techniques can be applied to preclinical relapse datasets to compare different treatment strategies and support decision-making on long-term regimen efficacy. The analysis highlighted the potential of data-driven approaches to extract complex patterns from experimental outcomes that are difficult to capture with traditional methods alone.
The work also emphasized how machine learning can complement mechanistic and Quantitative Systems Pharmacology (QSP) modeling, helping bridge preclinical evidence with translational and clinically relevant insights. By integrating these approaches, researchers can improve the interpretation of relapse data and better inform the design and evaluation of therapeutic strategies.
We thank Roberto Visintainer for sharing this work and for fostering a stimulating discussion within the COSBI research community.
